Systems and Methods for Self-Supervised Depth Estimation

2. The method of claim 1, further comprising reiterating the operations of warping the first image, projecting the warped first image and determining the loss using updated predicted depth values for the first image.

3. The method of claim 1, wherein projecting is performed using a neural camera model to model intrinsic parameters of the first camera.

4. The method of claim 1, further comprising predicting a transformation from the first camera mounting location to the second camera mounting location based on loss calculations between the warped first image and the reference image.

5. The method of claim 1, wherein the reference image is an image captured at a time, t+/−1, different from a time, t, at which the first image is captured.

6. The method of claim 5, wherein a transformation from the first camera mounting location to the second camera mounting location includes movement of the vehicle between times t and t+/−1.

7. The method of claim 1, wherein projecting the warped first image onto the reference image comprises lifting 2D points of the warped first image to 3D points, determining a transformation between the first camera mounting location and the second camera mounting location and using the transformation to project the 3D points onto the reference image in 2D.

8. The method of claim 7, wherein the transformation comprises a distance in three dimensions between image sensors of the first camera mounting location and the second camera mounting location.

10. The system of claim 9, wherein the operations further comprise reiterating the operations of warping the first image, projecting the warped first image and determining the loss using updated predicted depth values for the first image.

11. The system of claim 9, wherein projecting is performed using a neural camera model to model intrinsic parameters of the first camera.

12. The system of claim 9, wherein the operations further comprise predicting a transformation from the first camera mounting location to the second camera mounting location based on loss calculations between the warped first image and the reference image.

13. The system of claim 9, wherein the reference image is an image captured at a time, t+/−1, different from a time, t, at which the first image is captured.

14. The system of claim 13, wherein a transformation from the first camera mounting location to the second camera mounting location includes movement of the vehicle between times t and t+/−1.

15. The system of claim 9, wherein projecting the warped first image onto the reference image comprises lifting 2D points of the warped first image to 3D points, determining a transformation between the first camera mounting location and the second camera mounting location and using the transformation to project the 3D points onto the reference image in 2D.

16. The system of claim 15, wherein the transformation comprises a distance in three dimensions between image sensors of the first camera mounting location and the second camera mounting location.

18. The system of claim 17, further comprising a neural camera model configured to model intrinsic parameters of the camera.

19. The system of claim 17, wherein the reference image is an image captured at a time, t+/−1, different from a time, t, at which the first image is captured.

20. The system of claim 19, wherein a transformation from the first camera mounting location to the second camera mounting location includes movement of the vehicle between times t and t+/−1.